We show examples from the semantic map datasets we used for training the potential function network in fig:gibsonsemanticmaps,fig:mp3dsemanticmaps. The Gibson semantic maps contain up to 15 object categories of which 6 categories are goal categories (same as [1]}). The MP3D semantic maps contain up to 21 object categories of which all are goal categories (same as the Habitat challenge [2]}). These maps are computed by performing an orthographic projection of the 3D point-cloud annotations (following [3]}). In addition to the pipeline from [3]}, we perform additional pre-processing to obtain per-floor maps. Specifically, we segment the 3D semantic point-cloud from Gibson [5]} and MP3D [6]} annotations into different floors. We do this by loading each scene into Habitat [7]}, identifying the navigable points and clustering them along the Y-coordinate to automatically discover the number of floors and their extents using DBScan [8]}. We then perform orthographic projection independently for each floor of the scene.
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